Optical transceiver tuning using machine learning

ABSTRACT

A test and measurement device has a connection to allow the test and measurement device to connect to an optical transceiver, one or more processors, configured to execute code that causes the one or more processors to: initially set operating parameters for the optical transceiver to average parameters, acquire a waveform from the optical transceiver, measure the acquired waveform and determine if operation of the transceiver passes or fails, send the waveform and the operating parameters to a machine learning system to obtain estimated parameters if the transceiver fails, adjust the operating parameters based upon the estimated parameters, and repeat the acquiring, measuring, sending, and adjusting as needed until the transceiver passes. A method to tune optical transceivers includes connecting a transceiver to a test and measurement device, setting operating parameters for the transceiver to an average set of parameters, acquiring a waveform from the transceiver, measuring the waveform to determine if the transceiver passes or fails, sending the waveform and operating parameters to a machine learning system when the transceiver fails, using the machine learning system to provide adjusted operating parameters, setting the operating parameters to the adjusted parameters, and repeating the acquiring, measuring, sending, using, and setting until the transceiver passes.

RELATED APPLICATIONS

This disclosure claims benefit of U.S. Provisional Patent ApplicationNo. 63/165,698 titled “OPTICAL TRANSMITTER TUNING USING MACHINELEARNING,” filed Mar. 24, 2021, which is incorporated herein in itsentirety.

TECHNICAL FIELD

This disclosure relates to test and measurement systems, and moreparticularly to systems and methods for optical transceiver tuning, forexample, during manufacturing.

BACKGROUND

Manufacturers of optical transceiver may take up to two hours to tunetheir optical transceiver, as a worst case example. They typicallyaccomplish this by sweeping the tuning parameters. In some cases, it maytake 200 iterations worst case, and three to five iterations best case.Speeding up this process to reduce the number of iterations to get thetransceiver tuned reducing both the time and therefore, the expense, ofmanufacturing.

Embodiments of the disclosed apparatus and methods address shortcomingsin the prior art.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of a manufacturing workflow for tuning opticaltransceiver parameters.

FIG. 2 shows a test and measurement device usable to tune opticaltransceivers.

FIG. 3 shows a flowchart of an embodiment of a process to train amachine learning system for tuning optical transceivers.

FIG. 4 shows a flowchart of an embodiment of a process to use machinelearning to tune optical transceivers.

FIG. 5 shows a flowchart of an embodiment of a portion of a process touse machine learning to tune optical transceivers.

FIG. 6 shows examples of tensor images derived from waveforms for use ina machine learning system.

DETAILED DESCRIPTION

Currently, optical transceiver tuning does not employ any type ofmachine learning to decrease the time needed to tune and test thetransceivers. The amount of time it takes to train them increases theoverall costs of the transceivers. A typical manufacturer workflow isshown in FIG. 1. At 10, the user connects the optical transceiver to atest and measurement device. As used here, the term “test andmeasurement device” includes any device capable of performing tests andmeasuring the resulting operation of the optical transceiver as a deviceunder test.

The user sets a temperature at 12 and waits until the temperaturestabilizes. The user then sets the voltage at 14. The user sets theoperating parameters for the DUT at 16, operates the DUT and thenmeasures its operation. Measurement may take many forms, as will bediscussed in more detail later. At 18, the system determines if themeasurement indicates that the part operates within a desired range ofthe measured values. If it operates with the desired range or ranges, at20, the transceiver passes at 22. The final temperature and voltage usedin the test may be stored and used for the next transceiver at 24 andthen the test completes for that part.

If the DUT is not tuned at 22, then the user may adjust the parametersat 16 and re-run the test back through 22. This process may be completedmany times until the DUT either passes the test or fails and the testcompletes at 26.

This is a time intensive, and ultimately expensive, process. Theembodiments here employ machine learning to both provide a more accuratestarting point and to adjust the parameters more efficiently.

The use of a machine learning process can decrease the amount of timeneeded to tune, test and determine if a part passes or fails. FIG. 2shows an embodiment of a test and measurement system or device that usesa machine learning system to determine operating parameters for theparts. The output measurements for which the user may tune, and will beused to measure the operation, may include transmitter and dispersioneye closure penalty quaternary (TDECQ), extinction ratio, averageoptical power, optical modulation amplitude, level separation mismatchratio, among others. The tuning parameters of the optical transceiversthat may be changed or adjusted may include such things as bias current,modulation voltage, and others.

The test and measurement device 30 may include many differentcomponents. FIG. 2 shows one possible embodiment and may include more orfewer components. The device of FIG. 2 connects to the DUT, in this casean optical transceiver 32. The test and measurement device, such as anoscilloscope, may include an optical/electrical (O/E) conversion module34 that connects to the test and measurement device through a connectionsuch as 36. The processor 38 represents one or more processors and willbe configured to execute code to cause the processor to perform varioustasks including the manufacturer's test automation code 40. Or, the testautomation code could be part of another computing device separate fromthe test and measurement device 30, with which the processor 38interacts. FIG. 2 shows the machine learning system 42 as part of thetest and measurement device. The machine learning system may also resideon a separate device such as a computer in communication with the testand measurement device.

For purposes of this discussion, the term “test and measurement device”as used here includes those embodiments that include an externalcomputing device as part of the test and measurement device. The memory44 may reside on the test and measurement device or be distributedbetween the test and measurement device and the computing device. Aswill be discussed in more detail later, the memory may contain trainingdata and run-time data used by the machine learning system. The test andmeasurement device may also include a user interface 46 that maycomprise one or more of a touch screen, video screen, knobs, buttons,lights, keyboard, mouse, etc.

In operation, the optical transceiver 32 connects to the test andmeasurement device through the O/E converter 34 and the connection 36. Atest automation process 40 sends the transceiver tuning parameters tothe transceiver. The transceiver parameters are received from themachine learning system 42, and may be based upon average parametersdeveloped through testing and training. The test and measurement deviceacquires a waveform from the transceiver DUT and returns the waveform tothe processor. The system measures the waveform and determines whetherthe transceiver has a pass or fail for that set of tuning parameters.

One measurement, mentioned above is TDECQ, which is using a PAM4 eyediagram. As discussed above, this comprises one of the performancemeasurements for which the transceiver may be tuned, with others thatmay include extinction ratio (ER), average optical power (AOP), opticalmodulation amplitude (OMA), level separation mismatch (RLM) ratio andothers.

The machine learning system 42 performs the run time selection andadjustment of the tuning parameters based upon its training. Thetraining process builds a training set of tuning parameters andassociated waveforms that can then inform the selection of the tuningparameters based upon the waveform acquired from the DUT. FIG. 3 shows aprocess of training a machine learning system that also providesstarting parameters for a run-time testing process.

In the below discussion, several terms have specific meanings. Intraining the machine learning system, the process requires a set ofparameters and associated waveforms for training. As discussed in detailbelow, the user optimally tunes a large number of transceivers thatresults in a set of parameters referred to here as “optimizedparameters” and contains the optimized parameters for each of the largenumber of transceivers. The term “average parameters” refers to a set ofparameters each of which represents the average of that particularparameter.

In a first iterative sub-process, a user connects an optical transceiverto the test and measurement device at 50. Typically, this processincudes allowing the temperature of the transceiver to stabilize. Thetransceiver undergoes standard tuning at 52, meaning any current tuningprocedure used before the implementation of the embodiments here. Once aparticular transceiver undergoes tuning to reach its tuned state, theparameters used at that state are the optimized parameter for thattransceiver. The optimized parameters from each of these iterations aregathered, such as into a histogram, at 56, and an average is determinedat 58.

As will be discussed in more detail with regard to FIG. 4 and therun-time environment, these averages will act as the initial startingparameters for the run-time process. This process continues until enoughtransceivers have undergone tuning as determined at 54. Thedetermination of how many transmitters are enough may depend upon thearchitecture of the machine learning system and how many the systemrequires to produce accurate results.

A second iterative sub-process begins at 60. The user connects thetransceiver, and lets the temperature stabilize. The transceiverundergoes tuning to a “next” set of parameters at 62, which for thefirst iteration comprises an initial set of parameters. The system alsorecords these parameters to be sent to the machine learning system. Eachiteration will sweep the parameters, meaning that the system adjustseach parameter to the next setting for that parameter until all settingsfor that parameter have completed.

The system captures the waveform at 64 for each set of parameters andwill also send the waveform to the machine learning system. As part ofcapturing the waveform, the system may perform clock recovery andgenerate a partial eye diagram diagram, also known as a short patternoverlay. In one embodiment, the partial eye diagram diagram may includeonly single level transitions, where a single-level transition means atransition from one level only to one other level one transition fromlow to high, high to low, etc, such as the separated single-levelpartial eye diagrams described in U.S. patent application Ser. No.17/592,437. This resulting overlay may comprise the waveform sent to themachine learning system, although the waveform may be represented inother formats or vector spaces in other embodiments. In one embodiment,as part of the waveform capture, the system also calculates a numbermeasurements, such as TDECQ, OMA, ER, AOP, RLM, as mentioned above. At66, the system sends the information gathered, including the histograms,averages, and the information specific to each iteration, such as thewaveform, short pattern overlay, measurements, to be associated witheach set of parameters to the machine learning system for processing.The machine learning system associates all of this information with thewaveform to train it to identify the most accurate parameters for thenext iteration. This process repeats until all of the iterations for theparameter being swept as checked at 68.

As part of the machine learning training process, the system undergoestesting and validation. The system will provide test waveforms to themachine learning system to have it generate predictions for the next setof parameters, then test those parameters to see if the machine learningsystem made an accurate prediction. The process of training continuesuntil enough transceivers have undergone screening at 70 until themachine learning system produces results with a desired accuracy. Oncethis has been achieved, the training process completes at 72 and thesystem can move to run-time use. One should note that the system mayreturn to the training process as needed, such as if the predictionaccuracy drops, or some change occurs in the nature of the transceiversbeing tested.

FIG. 4 shows an embodiment of a method of testing optical transceiversin the runtime environment. At 80, a user connects the transceiver tothe test and measurement device and allows the temperature to stabilize.The temperature may comprise one of the adjustments made and multipleiterations may include different temperatures. The system tunes thetransceiver to a set of operating parameters at 82. As used here, theterm “operating parameters” means the parameters set for thetransceiver. The operating parameters may change depending upon thepoint in the process. Initially, the process sets the operatingparameters to the average parameters developed in the first part of thetraining process. The test and measurement device acquires a waveformfrom the transceiver at 84. As in the training process, this may includeperforming clock recovery and generating a partial eye diagram overlaywith only single level transitions. The system may also performmeasurements such as TDECQ, ER, AOP, OMA, RLM and others at 86. If theresults indicate that the operation of the transceiver lies within adesired range at 88, the transceiver passes at 90.

In one embodiment, the measurement and determination of pass fail shownin FIG. 4, the process performs clock recovery 110 and from thatgenerates tensor images at 111, as shown in FIG. 5. An example tensorimage is shown in FIG. 6. FIG. 6 shows the input waveforms on the left,with the three channels showing the input waveforms on the Red channel120, Blue channel 122 and Green channel 124. The tensor 130 and thetensors in the same position across the figure correspond to the Redchannel, the position of tensor 132 corresponds to the Blue channel, andthe position of tensor 134 corresponds to the Green channel. Shortpattern images created from the acquired waveform may be placed into allthree color channels. Different code sequences may be placed intodifferent color channels. Many combinations of patterns and channels arepossible.

The tensor images are sent to the machine learning system as thewaveform images used in the machine learning system. The process thenmeasures the waveforms at 112 at FIG. 5 and these are then checked tosee if they are in range at 114. The process then returns to FIG. 4 at88.

Referring back to FIG. 3, for this embodiment, this process of capturingthe waveform, would include generating an image and tensors to be usedin the machine learning system. The machine learning system would betrained with these types of tensor images. This allows the system to usethe tensor images in the machine learning system at runtime.

If the results show that the transceiver does not operate within thedesired range of whatever measurement(s) used, the transceiver fails.The system may check to see how many times the transceiver has undergonethe process at 92. If the number of times is under a predeterminednumber, the results are sent to the machine learning system at 94 fordetermination of a next set of operating parameters. The machinelearning system then provides estimated parameters for the nextiteration. In one embodiment, the operating parameters for thetransceiver in the next iteration comprise “adjusted parameters”resulting from subtracting the estimated parameters from the previousoperating parameters to find a difference, and then adding thedifference back to the average parameters to produce new operatingparameters.

Returning to 92, if the machine learning process has repeated a numberof times and the transceiver continues to fail, the system uses the“standard” procedure for tuning at 96. This comprises any tuningprocedure previously used prior to the implementation of the embodimentshere. The process then continues until the operation of the transceivermeets the desired measurements and at 98 and the transceiver passes at90. However, if even after the standard tuning process, the transceivercannot be adjusted to operate within the desired range, the transceiveris failed at 100.

In this manner, a machine learning system can increase the efficiency ofthe tuning process. This allows the system to pass or fail thetransceivers more quickly and reduces both the time and expense of themanufacturing process.

Aspects of the disclosure may operate on a particularly createdhardware, on firmware, digital signal processors, or on a speciallyprogrammed general purpose computer including a processor operatingaccording to programmed instructions. The terms controller or processoras used herein are intended to include microprocessors, microcomputers,Application Specific Integrated Circuits (ASICs), and dedicated hardwarecontrollers. One or more aspects of the disclosure may be embodied incomputer-usable data and computer-executable instructions, such as inone or more program modules, executed by one or more computers(including monitoring modules), or other devices. Generally, programmodules include routines, programs, objects, components, datastructures, etc. that perform particular tasks or implement particularabstract data types when executed by a processor in a computer or otherdevice. The computer executable instructions may be stored on anon-transitory computer readable medium such as a hard disk, opticaldisk, removable storage media, solid state memory, Random Access Memory(RAM), etc. As will be appreciated by one of skill in the art, thefunctionality of the program modules may be combined or distributed asdesired in various aspects. In addition, the functionality may beembodied in whole or in part in firmware or hardware equivalents such asintegrated circuits, FPGA, and the like. Particular data structures maybe used to more effectively implement one or more aspects of thedisclosure, and such data structures are contemplated within the scopeof computer executable instructions and computer-usable data describedherein.

The disclosed aspects may be implemented, in some cases, in hardware,firmware, software, or any combination thereof. The disclosed aspectsmay also be implemented as instructions carried by or stored on one ormore or non-transitory computer-readable media, which may be read andexecuted by one or more processors. Such instructions may be referred toas a computer program product. Computer-readable media, as discussedherein, means any media that can be accessed by a computing device. Byway of example, and not limitation, computer-readable media may comprisecomputer storage media and communication media.

Computer storage media means any medium that can be used to storecomputer-readable information. By way of example, and not limitation,computer storage media may include RAM, ROM, Electrically ErasableProgrammable Read-Only Memory (EEPROM), flash memory or other memorytechnology, Compact Disc Read Only Memory (CD-ROM), Digital Video Disc(DVD), or other optical disk storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, and any othervolatile or nonvolatile, removable or non-removable media implemented inany technology. Computer storage media excludes signals per se andtransitory forms of signal transmission.

Communication media means any media that can be used for thecommunication of computer-readable information. By way of example, andnot limitation, communication media may include coaxial cables,fiber-optic cables, air, or any other media suitable for thecommunication of electrical, optical, Radio Frequency (RF), infrared,acoustic or other types of signals.

Additionally, this written description makes reference to particularfeatures. It is to be understood that the disclosure in thisspecification includes all possible combinations of those particularfeatures. For example, where a particular feature is disclosed in thecontext of a particular aspect, that feature can also be used, to theextent possible, in the context of other aspects.

Also, when reference is made in this application to a method having twoor more defined steps or operations, the defined steps or operations canbe carried out in any order or simultaneously, unless the contextexcludes those possibilities.

Examples

Illustrative examples of the disclosed technologies are provided below.An embodiment of the technologies may include one or more, and anycombination of, the examples described below.

Example 1 is a method to tune optical transceivers, comprising:connecting a transceiver to a test and measurement device; settingoperating parameters for the transceiver to an average set ofparameters; acquiring a waveform from the transceiver; measuring thewaveform to determine if the transceiver passes or fails; sending thewaveform and operating parameters to a machine learning system when thetransceiver fails; using the machine learning system to provide adjustedoperating parameters; setting the operating parameters to the adjustedparameters; and repeating the acquiring, sending, using, and settinguntil the transceiver passes.

Example 2 is the method of Example 1, further comprising setting atemperature for the transceiver.

Example 3 is the method of Example 2, further comprising repeating themethod multiple times each for a different temperature.

Example 4 is the method of any of Examples 1 through 3, whereinproviding adjusted operating parameters comprises: subtracting estimatedparameters provided from the machine learning system from the operatingparameters to find a difference; and adding the difference to theoperating parameters to produce new operating parameters.

Example 5 is the method of any of Examples 1 through 4, furthercomprising completing testing of the transceiver if the transceiverpasses.

Example 6 is the method of any of Examples 1 through 5, whereinrepeating the acquiring and sending comprises: repeating the acquiringand sending until a predetermined number of times has been reachedwithout the transceiver passing; and testing the transceiver using adifferent method.

Example 7 is the method of any of Examples 1 through 6, wherein thewaveform is represented as an eye diagram consisting of single-leveltransitions.

Example 8 is of any of Examples 1 through 7, wherein measuring thewaveform comprises measuring a transmitter dispersion eye closurequaternary (TDECQ) value.

Example 9 is a test and measurement device, comprising: a connection toallow the test and measurement device to connect to an opticaltransceiver; and one or more processors, configured to execute code thatcauses the one or more processors to: initially set operating parametersfor the optical transceiver to average parameters; acquire a waveformfrom the optical transceiver; measure the acquired waveform anddetermine if operation of the transceiver passes or fails; send thewaveform and the operating parameters to a machine learning system toobtain estimated parameters if the transceiver fails; adjust theoperating parameters based upon the estimated parameters; and repeat theacquiring, measuring, sending, and adjusting as needed until thetransceiver passes.

Example 10 is the device of Example 9, wherein the one or moreprocessors are further configured to execute code to cause the one ormore processors to set a temperature for the transceiver.

Example 11 is the device of Example 10, wherein the one or moreprocessors are further configured to cause the one or more processors torepeat the execution of the code multiple times each for a differenttemperature.

Example 12 is the device of Example 10, wherein the code to cause theone or more processors to adjust the operating parameters comprises codeto cause the one or more processors to subtract the estimated parametersfrom the operating parameters to find a difference, and add thedifference to the average parameters to produce new operatingparameters.

Example 13 is a method of training a machine learning system todetermine operating parameters for optical transceivers, comprising:connecting the transceiver to a test and measurement device; tuning thetransceiver with a set of parameters; capturing a waveform from thetransceiver; sending the waveform and the set of parameters to a machinelearning system; and repeating the tuning, capturing and sending until asufficient number of samples are gathered.

Example 14 is the method of Example 13, further comprising: furthercomprising: setting a temperature for the transceiver; waiting until thetemperature stabilizes; and recording the parameters.

Example 15 is the method of Example 14, further comprising adding theset of parameters to a histogram of parameters for the temperature.

Example 16 is the method of Example 14, further comprising adding theset of parameters to a histogram of parameters for the temperature.

Example 17 is the method of Example 14, further comprising repeating thesetting, waiting and recording until a sufficient number of samples aregathered.

Example 18 is the method of any of Examples 13 through 17, whereincapturing the waveform further comprises generating an eye diagramoverlay containing only single-level transitions.

Example 19 is the method of any of Examples 13 through 18, wherein therepeating occurs after changing the parameters to sweep a range for eachparameter in the set of parameters.

All features disclosed in the specification, including the claims,abstract, and drawings, and all the steps in any method or processdisclosed, may be combined in any combination, except combinations whereat least some of such features and/or steps are mutually exclusive. Eachfeature disclosed in the specification, including the claims, abstract,and drawings, can be replaced by alternative features serving the same,equivalent, or similar purpose, unless expressly stated otherwise.

Although specific embodiments have been illustrated and described forpurposes of illustration, it will be understood that variousmodifications may be made without departing from the spirit and scope ofthe disclosure. Accordingly, the invention should not be limited exceptas by the appended claims.

1. A method to tune optical transceivers, comprising: connecting atransceiver to a test and measurement device; setting operatingparameters for the transceiver to an average set of parameters;acquiring a waveform from the transceiver; measuring the waveform todetermine if the transceiver passes or fails; sending the waveform andoperating parameters to a machine learning system when the transceiverfails; using the machine learning system to provide adjusted operatingparameters; setting the operating parameters to the adjusted parameters;and repeating the acquiring, measuring, sending, using, and settinguntil the transceiver passes.
 2. The method as claimed in claim 1,further comprising setting a temperature for the transceiver.
 3. Themethod as claimed in claim 2, further comprising repeating the methodmultiple times each for a different temperature.
 4. The method asclaimed in claim 1, wherein providing adjusted operating parameterscomprises: subtracting estimated parameters provided from the machinelearning system from the operating parameters to find a difference; andadding the difference to the operating parameters to produce newoperating parameters.
 5. The method as claimed in claim 1, furthercomprising completing testing of the transceiver if the transceiverpasses.
 6. The method as claimed in claim 1, wherein repeating theacquiring and sending comprises: repeating the acquiring and sendinguntil a predetermined number of times has been reached without thetransceiver passing; and testing the transceiver using a differentmethod.
 7. The method as claimed in claim 1, wherein the waveform isrepresented as an eye diagram consisting of single-level signaltransitions.
 8. The method as claimed in claim 1, wherein measuring thewaveform comprises measuring a transmitter dispersion eye closurequaternary (TDECQ) value.
 9. A test and measurement device, comprising:a connection to allow the test and measurement device to connect to anoptical transceiver; and one or more processors, configured to executecode that causes the one or more processors to: initially set operatingparameters for the optical transceiver to average parameters; acquire awaveform from the optical transceiver; measure the acquired waveform anddetermine if operation of the transceiver passes or fails; send thewaveform and the operating parameters to a machine learning system toobtain estimated parameters if the transceiver fails; adjust theoperating parameters based upon the estimated parameters; and repeat theacquiring, measuring, sending, and adjusting as needed until thetransceiver passes.
 10. The device as claimed in claim 9, wherein theone or more processors are further configured to execute code to causethe one or more processors to set a temperature for the transceiver. 11.The device as claimed in claim 10, wherein the one or more processorsare further configured to cause the one or more processors to repeat theexecution of the code multiple times each for a different temperature.12. The device as claimed in claim 10, wherein the code to cause the oneor more processors to adjust the operating parameters comprises code tocause the one or more processors to subtract the estimated parametersfrom the operating parameters to find a difference, and add thedifference to the average parameters to produce new operatingparameters.
 13. A method of training a machine learning system todetermine operating parameters for optical transceivers, comprising:connecting the transceiver to a test and measurement device; tuning thetransceiver with a set of parameters; capturing a waveform from thetransceiver; sending the waveform and the set of parameters to a machinelearning system; and repeating the tuning, capturing, and sending untila sufficient number of samples are gathered.
 14. The method as claimedin claim 13, further comprising: setting a temperature for thetransceiver; waiting until the temperature stabilizes; and recording theparameters.
 15. The method as claimed in claim 14, further comprisingadding the set of parameters to a histogram of parameters for thetemperature.
 16. The method as claimed in claim 14, further comprisingadding the set of parameters to a histogram of parameters for thetemperature.
 17. The method as claimed in claim 14, further comprisingrepeating the setting, waiting and recording until a sufficient numberof samples are gathered.
 18. The method as claimed in claim 13, whereincapturing the waveform further comprises generating an eye diagramoverlay containing only single-level transitions.
 19. The method asclaimed in claim 13, wherein the repeating occurs after changing theparameters to sweep a range for each parameter in the set of parameters.